Time-sensitive predictors of embolism in patients with left-sided endocarditis: Cohort study
Bibliographic record
Abstract
INTRODUCTION: Accurate prediction of embolic events in infective endocarditis could inform critical clinical decisions, such as the timing of cardiac surgical intervention. However, many embolic events occur before hospital admission and echocardiography and are thus non-modifiable. We aimed to identify time-sensitive variables that predict embolic events in infective endocarditis, focusing on those that occur after diagnosis. METHODS: Clinical, microbiological, and echocardiographic characteristics were collected from 116 patients with definite or probable left-sided infective endocarditis admitted to Sunnybrook Health Sciences Centre (Toronto, Canada) between October 2013 and July 2016; associations between these characteristics and embolic events were identified using simple logistic regression. RESULTS: The mean (SD) age was 66 (17) years; 82 patients (71%) were men. The most frequent microorganisms were Staphylococcus aureus (23%) and viridans group streptococci (21%). Seventy-nine (68%) patients had left-sided vegetations, with involvement of the aortic valve in 34 (43%) patients, mitral valve in 37 (47%) patients, and both in 8 (10%) patients. The mean (SD) vegetation size was 10 (7) mm. Forty-three unique patients (37%) had 50 embolic events, with most (34/43; 79%) having a first embolic event (38/50; 76%) before or on the day of echocardiography. There were no significant predictors of the 11 patients with an embolic event after echocardiography; significant predictors of an embolic event at any time were single valve vegetation vs. no vegetation (OR, 4.75; 95% confidence interval [CI], 1.76-12.78) and, among patients with a vegetation, mitral vs. aortic valve location (OR, 4.43; 95%CI, 1.63-12.04). CONCLUSIONS: Associations between patient and echocardiographic characteristics and embolism in patients with infective endocarditis may be time-sensitive, as few embolic events occurred after clinical and echocardiographic assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".